We propose ISS-RegAuth, a lightweight indoor space authentication framework that authenticates a user by comparing LiDAR captures of personal rooms. Prior work processes every point in the cloud, where planar surfaces such as walls and floors dominate similarity calculations, causing latency and potential privacy exposure. In contrast, ISS-RegAuth retains only 1-2\% of Intrinsic Shape Signatures (ISS) keypoints, computes their Fast Point Feature Histograms, and performs RANSAC and ICP on this sparse set. On 100 ARKitScenes pairs, this approach reduces the equal-error rate from 0.02 to 0.00, cuts processing time by 20\%, and lowers transmitted data to 2.2\% of the original. These results show that keypoint-based sparse representation can make privacy-preserving, edge-deployable indoor space authentication practical. As an early step, this work opens a path toward device-independent authentication and account-recovery mechanisms that rely on users' physical environments.

翻译：我们提出ISS-RegAuth，一种轻量级室内空间认证框架，通过比对个人房间的LiDAR扫描数据实现用户认证。先前方法需处理点云中所有点，其中墙面与地板等平面结构主导相似度计算，导致延迟并可能暴露隐私。与之相对，ISS-RegAuth仅保留1-2%的本征形状特征（ISS）关键点，计算其快速点特征直方图，并在此稀疏集上执行RANSAC与ICP算法。在100组ARKitScenes数据对上，该方法将等错误率从0.02降至0.00，处理时间缩短20%，传输数据量降至原始数据的2.2%。实验结果表明，基于关键点的稀疏表征能够实现隐私保护且可部署于边缘设备的室内空间认证系统。作为早期探索，本研究为构建设备无关、依托用户物理环境的认证与账户恢复机制开辟了新路径。